{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:59:36.487660Z",
     "start_time": "2023-07-05T11:59:36.099975Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "import statsmodels.api as sm\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:59:36.500783Z",
     "start_time": "2023-07-05T11:59:36.488439Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(200, 5)\n"
     ]
    },
    {
     "data": {
      "text/plain": "   Unnamed: 0     TV  radio  newspaper  sales\n0           1  230.1   37.8       69.2   22.1\n1           2   44.5   39.3       45.1   10.4\n2           3   17.2   45.9       69.3    9.3\n3           4  151.5   41.3       58.5   18.5\n4           5  180.8   10.8       58.4   12.9",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>TV</th>\n      <th>radio</th>\n      <th>newspaper</th>\n      <th>sales</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>230.1</td>\n      <td>37.8</td>\n      <td>69.2</td>\n      <td>22.1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>44.5</td>\n      <td>39.3</td>\n      <td>45.1</td>\n      <td>10.4</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>17.2</td>\n      <td>45.9</td>\n      <td>69.3</td>\n      <td>9.3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>151.5</td>\n      <td>41.3</td>\n      <td>58.5</td>\n      <td>18.5</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>180.8</td>\n      <td>10.8</td>\n      <td>58.4</td>\n      <td>12.9</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(r'../data/Advertising.csv')\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:59:36.508923Z",
     "start_time": "2023-07-05T11:59:36.505968Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "      TV  radio  newspaper  sales\n0  230.1   37.8       69.2   22.1\n1   44.5   39.3       45.1   10.4\n2   17.2   45.9       69.3    9.3\n3  151.5   41.3       58.5   18.5\n4  180.8   10.8       58.4   12.9",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>TV</th>\n      <th>radio</th>\n      <th>newspaper</th>\n      <th>sales</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>230.1</td>\n      <td>37.8</td>\n      <td>69.2</td>\n      <td>22.1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>44.5</td>\n      <td>39.3</td>\n      <td>45.1</td>\n      <td>10.4</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>17.2</td>\n      <td>45.9</td>\n      <td>69.3</td>\n      <td>9.3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>151.5</td>\n      <td>41.3</td>\n      <td>58.5</td>\n      <td>18.5</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>180.8</td>\n      <td>10.8</td>\n      <td>58.4</td>\n      <td>12.9</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.iloc[:,1:]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:59:36.513017Z",
     "start_time": "2023-07-05T11:59:36.510571Z"
    }
   },
   "outputs": [],
   "source": [
    "def fit_degree(data,var,target,degree):\n",
    "    poly = PolynomialFeatures(degree)\n",
    "    poly_data = poly.fit_transform(data[var].to_frame())\n",
    "    lin_model = LinearRegression(fit_intercept=False)\n",
    "    lin_model.fit(poly_data,data[target])\n",
    "    pred = lin_model.predict(poly_data)\n",
    "    \n",
    "    return pred\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.Is there a relationship between advertising sales and budget?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For tv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:59:36.639908Z",
     "start_time": "2023-07-05T11:59:36.517319Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape -  (200, 2)\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  sales   R-squared:                       0.612\n",
      "Model:                            OLS   Adj. R-squared:                  0.610\n",
      "Method:                 Least Squares   F-statistic:                     312.1\n",
      "Date:                Wed, 05 Jul 2023   Prob (F-statistic):           1.47e-42\n",
      "Time:                        19:59:36   Log-Likelihood:                -519.05\n",
      "No. Observations:                 200   AIC:                             1042.\n",
      "Df Residuals:                     198   BIC:                             1049.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          7.0326      0.458     15.360      0.000       6.130       7.935\n",
      "x1             0.0475      0.003     17.668      0.000       0.042       0.053\n",
      "==============================================================================\n",
      "Omnibus:                        0.531   Durbin-Watson:                   1.935\n",
      "Prob(Omnibus):                  0.767   Jarque-Bera (JB):                0.669\n",
      "Skew:                          -0.089   Prob(JB):                        0.716\n",
      "Kurtosis:                       2.779   Cond. No.                         338.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "poly = PolynomialFeatures(1)\n",
    "tv_data = poly.fit_transform(data['TV'].to_frame())\n",
    "print('shape - ',tv_data.shape)\n",
    "est = sm.OLS(data['sales'],tv_data)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:59:36.838127Z",
     "start_time": "2023-07-05T11:59:36.529182Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1400x800 with 1 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_1 = fit_degree(data,'TV','sales',1)\n",
    "plt.figure(figsize = (14,8))\n",
    "plt.scatter(data['TV'],data['sales'])\n",
    "plt.plot(data['TV'],pred_1,color = 'orange')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For radio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:59:36.838579Z",
     "start_time": "2023-07-05T11:59:36.711038Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape -  (200, 2)\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  sales   R-squared:                       0.332\n",
      "Model:                            OLS   Adj. R-squared:                  0.329\n",
      "Method:                 Least Squares   F-statistic:                     98.42\n",
      "Date:                Wed, 05 Jul 2023   Prob (F-statistic):           4.35e-19\n",
      "Time:                        19:59:36   Log-Likelihood:                -573.34\n",
      "No. Observations:                 200   AIC:                             1151.\n",
      "Df Residuals:                     198   BIC:                             1157.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          9.3116      0.563     16.542      0.000       8.202      10.422\n",
      "x1             0.2025      0.020      9.921      0.000       0.162       0.243\n",
      "==============================================================================\n",
      "Omnibus:                       19.358   Durbin-Watson:                   1.946\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               21.910\n",
      "Skew:                          -0.764   Prob(JB):                     1.75e-05\n",
      "Kurtosis:                       3.544   Cond. No.                         51.4\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "poly = PolynomialFeatures(1)\n",
    "radio_data = poly.fit_transform(data['radio'].to_frame())\n",
    "print('shape - ',radio_data.shape)\n",
    "est = sm.OLS(data['sales'],radio_data)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:59:37.010607Z",
     "start_time": "2023-07-05T11:59:36.751403Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1400x800 with 1 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_1 = fit_degree(data,'radio','sales',1)\n",
    "plt.figure(figsize = (14,8))\n",
    "plt.scatter(data['radio'],data['sales'])\n",
    "plt.plot(data['radio'],pred_1,color = 'orange')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### for newspaper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:59:37.011126Z",
     "start_time": "2023-07-05T11:59:36.887018Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape -  (200, 2)\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  sales   R-squared:                       0.052\n",
      "Model:                            OLS   Adj. R-squared:                  0.047\n",
      "Method:                 Least Squares   F-statistic:                     10.89\n",
      "Date:                Wed, 05 Jul 2023   Prob (F-statistic):            0.00115\n",
      "Time:                        19:59:36   Log-Likelihood:                -608.34\n",
      "No. Observations:                 200   AIC:                             1221.\n",
      "Df Residuals:                     198   BIC:                             1227.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         12.3514      0.621     19.876      0.000      11.126      13.577\n",
      "x1             0.0547      0.017      3.300      0.001       0.022       0.087\n",
      "==============================================================================\n",
      "Omnibus:                        6.231   Durbin-Watson:                   1.983\n",
      "Prob(Omnibus):                  0.044   Jarque-Bera (JB):                5.483\n",
      "Skew:                           0.330   Prob(JB):                       0.0645\n",
      "Kurtosis:                       2.527   Cond. No.                         64.7\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "poly = PolynomialFeatures(1)\n",
    "news_data = poly.fit_transform(data['newspaper'].to_frame())\n",
    "print('shape - ',news_data.shape)\n",
    "est = sm.OLS(data['sales'],news_data)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:59:37.024687Z",
     "start_time": "2023-07-05T11:59:36.901622Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1400x800 with 1 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_1 = fit_degree(data,'newspaper','sales',1)\n",
    "plt.figure(figsize = (14,8))\n",
    "plt.scatter(data['newspaper'],data['sales'])\n",
    "plt.plot(data['newspaper'],pred_1,color = 'orange')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How strong is the relationship?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:59:37.214573Z",
     "start_time": "2023-07-05T11:59:37.026784Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape -  (200, 4)\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  sales   R-squared:                       0.897\n",
      "Model:                            OLS   Adj. R-squared:                  0.896\n",
      "Method:                 Least Squares   F-statistic:                     570.3\n",
      "Date:                Wed, 05 Jul 2023   Prob (F-statistic):           1.58e-96\n",
      "Time:                        19:59:37   Log-Likelihood:                -386.18\n",
      "No. Observations:                 200   AIC:                             780.4\n",
      "Df Residuals:                     196   BIC:                             793.6\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "constant       2.9389      0.312      9.422      0.000       2.324       3.554\n",
      "TV             0.0458      0.001     32.809      0.000       0.043       0.049\n",
      "radio          0.1885      0.009     21.893      0.000       0.172       0.206\n",
      "newspaper     -0.0010      0.006     -0.177      0.860      -0.013       0.011\n",
      "==============================================================================\n",
      "Omnibus:                       60.414   Durbin-Watson:                   2.084\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              151.241\n",
      "Skew:                          -1.327   Prob(JB):                     1.44e-33\n",
      "Kurtosis:                       6.332   Cond. No.                         454.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "features = data.iloc[:,:3]\n",
    "columns = ['constant'] + list(features.columns)\n",
    "constant = pd.Series([1]*data.shape[0])\n",
    "features = pd.concat([constant,features],axis = 1)\n",
    "features.columns = columns\n",
    "\n",
    "print('shape - ',features.shape)\n",
    "est = sm.OLS(data['sales'],features)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# which media corresponds to sale?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:59:37.214824Z",
     "start_time": "2023-07-05T11:59:37.038070Z"
    }
   },
   "outputs": [],
   "source": [
    "# we can see the p value for the newspaper is very high, but for tv and radio the p value is significant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:59:37.215012Z",
     "start_time": "2023-07-05T11:59:37.040949Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "                 TV     radio  newspaper     sales\nTV         1.000000  0.054809   0.056648  0.782224\nradio      0.054809  1.000000   0.354104  0.576223\nnewspaper  0.056648  0.354104   1.000000  0.228299\nsales      0.782224  0.576223   0.228299  1.000000",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>TV</th>\n      <th>radio</th>\n      <th>newspaper</th>\n      <th>sales</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>TV</th>\n      <td>1.000000</td>\n      <td>0.054809</td>\n      <td>0.056648</td>\n      <td>0.782224</td>\n    </tr>\n    <tr>\n      <th>radio</th>\n      <td>0.054809</td>\n      <td>1.000000</td>\n      <td>0.354104</td>\n      <td>0.576223</td>\n    </tr>\n    <tr>\n      <th>newspaper</th>\n      <td>0.056648</td>\n      <td>0.354104</td>\n      <td>1.000000</td>\n      <td>0.228299</td>\n    </tr>\n    <tr>\n      <th>sales</th>\n      <td>0.782224</td>\n      <td>0.576223</td>\n      <td>0.228299</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = data.corr()\n",
    "corr_matrix\n",
    "#radio and newpaper are highly correlated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:59:37.215375Z",
     "start_time": "2023-07-05T11:59:37.061643Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape -  (200, 3)\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  sales   R-squared:                       0.897\n",
      "Model:                            OLS   Adj. R-squared:                  0.896\n",
      "Method:                 Least Squares   F-statistic:                     859.6\n",
      "Date:                Wed, 05 Jul 2023   Prob (F-statistic):           4.83e-98\n",
      "Time:                        19:59:37   Log-Likelihood:                -386.20\n",
      "No. Observations:                 200   AIC:                             778.4\n",
      "Df Residuals:                     197   BIC:                             788.3\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "constant       2.9211      0.294      9.919      0.000       2.340       3.502\n",
      "TV             0.0458      0.001     32.909      0.000       0.043       0.048\n",
      "radio          0.1880      0.008     23.382      0.000       0.172       0.204\n",
      "==============================================================================\n",
      "Omnibus:                       60.022   Durbin-Watson:                   2.081\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              148.679\n",
      "Skew:                          -1.323   Prob(JB):                     5.19e-33\n",
      "Kurtosis:                       6.292   Cond. No.                         425.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "#removing newspaper as a feature\n",
    "features = data.iloc[:,:2]\n",
    "columns = ['constant'] + list(features.columns)\n",
    "constant = pd.Series([1]*data.shape[0])\n",
    "features = pd.concat([constant,features],axis = 1)\n",
    "features.columns = columns\n",
    "\n",
    "print('shape - ',features.shape)\n",
    "est = sm.OLS(data['sales'],features)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Is the relationship linear?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:59:37.215575Z",
     "start_time": "2023-07-05T11:59:37.068441Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "LinearRegression(fit_intercept=False)",
      "text/html": "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression(fit_intercept=False)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression(fit_intercept=False)</pre></div></div></div></div></div>"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_model = LinearRegression(fit_intercept=False)\n",
    "lin_model.fit(features,data['sales'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:59:37.215857Z",
     "start_time": "2023-07-05T11:59:37.078663Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "array([2.92109991, 0.04575482, 0.18799423])"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:59:37.215928Z",
     "start_time": "2023-07-05T11:59:37.087343Z"
    }
   },
   "outputs": [],
   "source": [
    "pred = lin_model.predict(features)\n",
    "residual = data['sales'] - pred "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:59:37.504826Z",
     "start_time": "2023-07-05T11:59:37.093322Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.lines.Line2D at 0x7f7cc81e1d90>"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x600 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,6))\n",
    "plt.ylim(-8, 8)\n",
    "sns.scatterplot(x = data['sales'],y = residual)\n",
    "plt.axhline(y = 0,linestyle = 'dashed',linewidth = 0.5,color = 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:59:37.530020Z",
     "start_time": "2023-07-05T11:59:37.381053Z"
    }
   },
   "outputs": [],
   "source": [
    "# we can see from the graph, that the dispersion is not very well spread, there is some kind of \n",
    "# pattern on the right hand side of the graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Is there synergy among the interaction media?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:59:37.530615Z",
     "start_time": "2023-07-05T11:59:37.403414Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "      TV  radio  newspaper  sales\n0  230.1   37.8       69.2   22.1\n1   44.5   39.3       45.1   10.4\n2   17.2   45.9       69.3    9.3\n3  151.5   41.3       58.5   18.5\n4  180.8   10.8       58.4   12.9",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>TV</th>\n      <th>radio</th>\n      <th>newspaper</th>\n      <th>sales</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>230.1</td>\n      <td>37.8</td>\n      <td>69.2</td>\n      <td>22.1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>44.5</td>\n      <td>39.3</td>\n      <td>45.1</td>\n      <td>10.4</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>17.2</td>\n      <td>45.9</td>\n      <td>69.3</td>\n      <td>9.3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>151.5</td>\n      <td>41.3</td>\n      <td>58.5</td>\n      <td>18.5</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>180.8</td>\n      <td>10.8</td>\n      <td>58.4</td>\n      <td>12.9</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:59:37.648766Z",
     "start_time": "2023-07-05T11:59:37.423260Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "      TV  radio  newspaper  sales  interaction\n0  230.1   37.8       69.2   22.1      8697.78\n1   44.5   39.3       45.1   10.4      1748.85\n2   17.2   45.9       69.3    9.3       789.48\n3  151.5   41.3       58.5   18.5      6256.95\n4  180.8   10.8       58.4   12.9      1952.64",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>TV</th>\n      <th>radio</th>\n      <th>newspaper</th>\n      <th>sales</th>\n      <th>interaction</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>230.1</td>\n      <td>37.8</td>\n      <td>69.2</td>\n      <td>22.1</td>\n      <td>8697.78</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>44.5</td>\n      <td>39.3</td>\n      <td>45.1</td>\n      <td>10.4</td>\n      <td>1748.85</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>17.2</td>\n      <td>45.9</td>\n      <td>69.3</td>\n      <td>9.3</td>\n      <td>789.48</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>151.5</td>\n      <td>41.3</td>\n      <td>58.5</td>\n      <td>18.5</td>\n      <td>6256.95</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>180.8</td>\n      <td>10.8</td>\n      <td>58.4</td>\n      <td>12.9</td>\n      <td>1952.64</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['interaction'] = data['TV']*data['radio']\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:59:37.650381Z",
     "start_time": "2023-07-05T11:59:37.441131Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape -  (200, 4)\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  sales   R-squared:                       0.968\n",
      "Model:                            OLS   Adj. R-squared:                  0.967\n",
      "Method:                 Least Squares   F-statistic:                     1963.\n",
      "Date:                Wed, 05 Jul 2023   Prob (F-statistic):          6.68e-146\n",
      "Time:                        19:59:37   Log-Likelihood:                -270.14\n",
      "No. Observations:                 200   AIC:                             548.3\n",
      "Df Residuals:                     196   BIC:                             561.5\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================\n",
      "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------\n",
      "constant        6.7502      0.248     27.233      0.000       6.261       7.239\n",
      "TV              0.0191      0.002     12.699      0.000       0.016       0.022\n",
      "radio           0.0289      0.009      3.241      0.001       0.011       0.046\n",
      "interaction     0.0011   5.24e-05     20.727      0.000       0.001       0.001\n",
      "==============================================================================\n",
      "Omnibus:                      128.132   Durbin-Watson:                   2.224\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1183.719\n",
      "Skew:                          -2.323   Prob(JB):                    9.09e-258\n",
      "Kurtosis:                      13.975   Cond. No.                     1.80e+04\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.8e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "features = data.drop(['newspaper','sales'],axis = 1)\n",
    "columns = ['constant'] + list(features.columns)\n",
    "constant = pd.Series([1]*data.shape[0])\n",
    "features = pd.concat([constant,features],axis = 1)\n",
    "features.columns = columns\n",
    "\n",
    "\n",
    "print('shape - ',features.shape)\n",
    "est = sm.OLS(data['sales'],features)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We can see that by just adding the interaction term we have got the R2 value of almost 97"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-05T11:59:37.650675Z",
     "start_time": "2023-07-05T11:59:37.469744Z"
    }
   },
   "outputs": [],
   "source": []
  }
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